The demand for image retrieval with text manipulation exists in many fields, such as e-commerce and Internet search. Deep metric learning methods are used by most researchers to calculate the similarity between the qu...The demand for image retrieval with text manipulation exists in many fields, such as e-commerce and Internet search. Deep metric learning methods are used by most researchers to calculate the similarity between the query and the candidate image by fusing the global feature of the query image and the text feature. However, the text usually corresponds to the local feature of the query image rather than the global feature. Therefore, in this paper, we propose a framework of image retrieval with text manipulation by local feature modification(LFM-IR) which can focus on the related image regions and attributes and perform modification. A spatial attention module and a channel attention module are designed to realize the semantic mapping between image and text. We achieve excellent performance on three benchmark datasets, namely Color-Shape-Size(CSS), Massachusetts Institute of Technology(MIT) States and Fashion200K(+8.3%, +0.7% and +4.6% in R@1).展开更多
Lysine Lipoylation is a protective and conserved Post Translational Modification(PTM)in proteomics research like prokaryotes and eukaryotes.It is connected with many biological processes and closely linked with many m...Lysine Lipoylation is a protective and conserved Post Translational Modification(PTM)in proteomics research like prokaryotes and eukaryotes.It is connected with many biological processes and closely linked with many metabolic diseases.To develop a perfect and accurate classification model for identifying lipoylation sites at the protein level,the computational methods and several other factors play a key role in this purpose.Usually,most of the techniques and different traditional experimental models have a very high cost.They are time-consuming;so,it is required to construct a predictor model to extract lysine lipoylation sites.This study proposes a model that could predict lysine lipoylation sites with the help of a classification method known as Artificial Neural Network(ANN).The ANN algorithm deals with the noise problem and imbalance classification in lipoylation sites dataset samples.As the result shows in ten-fold cross-validation,a brilliant performance is achieved through the predictor model with an accuracy of 99.88%,and also achieved 0.9976 as the highest value of MCC.So,the predictor model is a very useful and helpful tool for lipoylation sites prediction.Some of the residues around lysine lipoylation sites play a vital part in prediction,as demonstrated during feature analysis.The wonderful results reported through the evaluation and prediction of this model can provide an informative and relative explanation for lipoylation and its molecular mechanisms.展开更多
故障预测及健康管理(prognostics and health management,PHM)对于增强系统的可靠性以及提高系统的可维护性具有重要意义。随着电力电子装置在各领域的广泛应用,研究电力电子装置的PHM技术势在必行。由于具有非侵入性的特点,基于混杂模...故障预测及健康管理(prognostics and health management,PHM)对于增强系统的可靠性以及提高系统的可维护性具有重要意义。随着电力电子装置在各领域的广泛应用,研究电力电子装置的PHM技术势在必行。由于具有非侵入性的特点,基于混杂模型的LC参数辨识方法是一种先进的PHM技术实现方式。然而,由于忽略了开关瞬态的二极管电流的突变,现有的混杂模型并不适用于Boost型电路。为了解决这一问题,对现有模型进行了修正,并提出一种基于小波去噪以及最小二乘算法的参数辨识方法。通过Matlab/Simulink仿真分析和实验验证,结果表明,该方法的参数辨识精度可达95%以上,验证了该修正模型的有效性。展开更多
基金Foundation items:Shanghai Sailing Program,China (No. 21YF1401300)Shanghai Science and Technology Innovation Action Plan,China (No.19511101802)Fundamental Research Funds for the Central Universities,China (No.2232021D-25)。
文摘The demand for image retrieval with text manipulation exists in many fields, such as e-commerce and Internet search. Deep metric learning methods are used by most researchers to calculate the similarity between the query and the candidate image by fusing the global feature of the query image and the text feature. However, the text usually corresponds to the local feature of the query image rather than the global feature. Therefore, in this paper, we propose a framework of image retrieval with text manipulation by local feature modification(LFM-IR) which can focus on the related image regions and attributes and perform modification. A spatial attention module and a channel attention module are designed to realize the semantic mapping between image and text. We achieve excellent performance on three benchmark datasets, namely Color-Shape-Size(CSS), Massachusetts Institute of Technology(MIT) States and Fashion200K(+8.3%, +0.7% and +4.6% in R@1).
文摘Lysine Lipoylation is a protective and conserved Post Translational Modification(PTM)in proteomics research like prokaryotes and eukaryotes.It is connected with many biological processes and closely linked with many metabolic diseases.To develop a perfect and accurate classification model for identifying lipoylation sites at the protein level,the computational methods and several other factors play a key role in this purpose.Usually,most of the techniques and different traditional experimental models have a very high cost.They are time-consuming;so,it is required to construct a predictor model to extract lysine lipoylation sites.This study proposes a model that could predict lysine lipoylation sites with the help of a classification method known as Artificial Neural Network(ANN).The ANN algorithm deals with the noise problem and imbalance classification in lipoylation sites dataset samples.As the result shows in ten-fold cross-validation,a brilliant performance is achieved through the predictor model with an accuracy of 99.88%,and also achieved 0.9976 as the highest value of MCC.So,the predictor model is a very useful and helpful tool for lipoylation sites prediction.Some of the residues around lysine lipoylation sites play a vital part in prediction,as demonstrated during feature analysis.The wonderful results reported through the evaluation and prediction of this model can provide an informative and relative explanation for lipoylation and its molecular mechanisms.
文摘故障预测及健康管理(prognostics and health management,PHM)对于增强系统的可靠性以及提高系统的可维护性具有重要意义。随着电力电子装置在各领域的广泛应用,研究电力电子装置的PHM技术势在必行。由于具有非侵入性的特点,基于混杂模型的LC参数辨识方法是一种先进的PHM技术实现方式。然而,由于忽略了开关瞬态的二极管电流的突变,现有的混杂模型并不适用于Boost型电路。为了解决这一问题,对现有模型进行了修正,并提出一种基于小波去噪以及最小二乘算法的参数辨识方法。通过Matlab/Simulink仿真分析和实验验证,结果表明,该方法的参数辨识精度可达95%以上,验证了该修正模型的有效性。
